CN111340104A - Method and device for generating control rule of intelligent device, electronic device and readable storage medium - Google Patents
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Abstract
The embodiment of the invention relates to the field of intelligent home furnishing, and discloses a method and a device for generating a control rule of intelligent equipment, electronic equipment and a computer readable storage medium. The method comprises the following steps: collecting at least one reported parameter record data set of first equipment and at least one user operation record data set of second equipment; respectively extracting characteristic points of the reported parameter record data set and the user operation record data set; clustering the reported parameter record data set to obtain a reported parameter record cluster set; clustering the user operation record data set to obtain a user operation record cluster set; calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and clustering the parameter record cluster set and the user operation record cluster set to obtain a device behavior related cluster set; converting the device behavior related cluster set into a device control rule; and outputting the equipment control rule.
Description
Technical Field
The embodiment of the invention relates to the field of smart home, in particular to a method and a device for generating control rules of smart equipment, electronic equipment and a computer-readable storage medium.
Background
Smart devices and smart homes have gradually come into the market and come into the view of the masses. The related range of brands, types, functions and the like of intelligent equipment on the market is also wider and wider. For example, cameras, intelligent door locks, smoke alarms, water immersion sensors, temperature and humidity sensors, door and window magnetic sensors, human body infrared sensors, gas sensors, audible and visual alarms, SOS emergency buttons and the like in home security monitoring and window pushing devices; air conditioners, refrigerators, water heaters, air purifiers, and the like in large-scale intelligent home appliances; the control manager is such as smart jack, infrared remote controller, intelligent switch, and the use of these smart machine has ensured the safety of family to a certain extent, has provided the life facility, has improved masses quality of life.
However, in most use scenarios of smart homes, remote control and remote monitoring are limited to each type of smart device individually. Linkage between the intelligent equipment, for example when smoke alarm detects that the smoke content in the room exceeds normal index and gives an alarm, the window is opened by remotely controlling the window pusher, and the like, and when the user receives the alarm of the smoke alarm, the window pusher is timely remotely operated to avoid the safety risk in the house. Or the user needs to set the linkage rule of the intelligent equipment according to the scene requirements of the intelligent home, so that the user is greatly required to have activity on the operation of the intelligent equipment, and obviously, not all users can meet the requirement.
At present, related technologies propose to analyze and learn the operation data of the intelligent device by a user, preset a corresponding linkage strategy on a platform by using a data analysis result, or record the operation behavior of the device of the user, and automatically send an operation instruction to the device by the user to achieve the intelligent and automatic goal of device linkage.
The inventors found that at least the following problems exist in the related art:
the behavior habit of the user is learned by recording the operation behavior record of the equipment, and the mode of automatically sending an operation instruction to the equipment instead of the user is replaced, so that the reason for operating the back of the equipment by the user is ignored. The user's operation of the device may be due to the user's subjective intent, such as issuing a turn-on command to the air conditioner soon after arriving at home; it is also possible that the windowing command is issued to the windower only if an alarm has occurred with the smoke alarm. These causes cannot be analyzed only from the operation records of the user, and may result in erroneous operation of the device.
Disclosure of Invention
An object of embodiments of the present invention is to provide a method and an apparatus for generating a control rule of an intelligent device, an electronic device, and a computer-readable storage medium, which can improve accuracy of a generated device control rule.
In order to solve the above technical problem, an embodiment of the present invention provides a method for generating a control rule of an intelligent device, including the following steps:
collecting at least one reported parameter record data set of first equipment and at least one user operation record data set of second equipment; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices;
respectively extracting characteristic points of the reported parameter record data set and the user operation record data set;
according to the extracted feature points, clustering the reported parameter record data set to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and carrying out cluster processing on the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set;
converting the device behavior related cluster set into a device control rule;
and outputting the equipment control rule.
The embodiment of the present invention further provides a device for generating a control rule of an intelligent device, including:
the collection unit is used for collecting at least one reporting parameter record data set of the first equipment and at least one user operation record data set of the second equipment; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices;
the extraction unit is used for respectively extracting the characteristic points of the reported parameter record data set and the user operation record data set;
the first clustering unit is used for clustering the reported parameter record data set according to the extracted feature points to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
the second clustering processing unit is used for calculating the correlation between the clustering points in the reported parameter record clustering set and the clustering points in the user operation record clustering set, and clustering the parameter record clustering set and the user operation record clustering set according to the correlation to obtain a device behavior related clustering set;
a rule conversion unit which converts the device behavior related cluster set into a device control rule;
an output unit that outputs the device control rule.
An embodiment of the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of generating control rules for a smart device.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the method for generating the control rule of the intelligent device when being executed by a processor.
Compared with the prior art, the method and the device have the advantages that the reported parameter record data set of at least one first device and the user operation record data set of at least one second device are collected; respectively extracting characteristic points of the reported parameter record data set and the user operation record data set; according to the extracted feature points, clustering the reported parameter record data set to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set; calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and carrying out cluster processing on the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set; converting the device behavior related cluster set into a device control rule; therefore, the driving force behind the behavior of the user operation equipment is explored by analyzing the incidence relation between the reported data of the equipment and the operation of the user equipment, and the driving force and the execution action of the equipment operation are converted into the equipment control rule, so that the accuracy of the equipment control rule is improved.
Additionally, the step of converting the set of device behavior related clusters into device control rules comprises:
in the device behavior related clustering set, selecting a preset number of clustering points according to the number and size sequence of data contained in the clustering points;
when the selected clustering point only contains user operation record data, generating a rule according to the user operation record data contained in the clustering point: controlling the second parameter of the second device to the second value at the second point in time;
when the selected clustering point contains reporting parameter record data and user operation record data, generating a linkage rule according to the reporting parameter record data and the user operation record data contained in the clustering point: and when the data value of the first parameter reported by the first equipment at the first time point is a first numerical value, issuing a linkage rule for controlling a second numerical value according to the parameter content to the second equipment at the second time point. In the embodiment, the association relationship between the behavior of the user and the equipment parameters is analyzed, so that the personalized equipment linkage rule is generated for the user, the intelligence of the whole intelligent home equipment control can be improved, and the complexity of the user in controlling the intelligent equipment in the intelligent home is simplified.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
Fig. 1 is a flowchart illustrating a method for generating a control rule of an intelligent device according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for generating control rules for smart devices according to an application scenario of the present invention;
FIG. 3 is a schematic diagram of an architecture of a device for generating control rules of a smart device according to another embodiment of the present invention;
fig. 4 is a schematic architecture diagram of an electronic device according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not constitute any limitation to the specific implementation manner of the present invention, and the embodiments may be mutually incorporated and referred to without contradiction.
The first embodiment of the present invention relates to a method for generating a control rule of an intelligent device. The flow is shown in fig. 1, and specifically comprises the following steps:
step 11, collecting at least one reported parameter record data set of a first device and at least one user operation record data set of a second device; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices; the reported parameter record data set includes a plurality of reported parameter record data, and each reported parameter record data includes information such as a first time point reported by the first device, a first parameter reported by the first device, a first value of the first parameter reported by the first device or a parameter state (for example, a switch state, a working state or a non-working state) of the first parameter, a function category to which the first device belongs, and a device identifier of the first device. The user operation record data set includes a plurality of user operation record data, and each user operation record data includes information such as a second time point for controlling the second device, a second parameter for controlling the second device, a parameter state of the second or second parameter after the second parameter is controlled, a function category to which the second device belongs, and a device identifier of the second device.
Step 12, respectively extracting feature points of the reported parameter record data set and the user operation record data set;
step 13, according to the extracted feature points, clustering the reported parameter record data set to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
step 14, calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and clustering the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set;
step 15, converting the device behavior related cluster set into a device control rule;
and step 16, outputting the equipment control rule. The device control rule may be recommended to the user, or other processing may be continued on the device control rule.
The first embodiment of the present invention relates to a method for generating a control rule of an intelligent device. The core of the embodiment lies in collecting at least one reported parameter record data set of a first device and at least one user operation record data set of a second device; respectively extracting characteristic points of the reported parameter record data set and the user operation record data set; according to the extracted feature points, clustering the reported parameter record data set to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set; calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and carrying out cluster processing on the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set; converting the device behavior related cluster set into a device control rule; and outputting the equipment control rule analysis, researching the driving force behind the behavior of the user operation equipment by analyzing the association relation between the reported data of the equipment and the operation of the user equipment, and converting the driving force and the execution action of the equipment operation into the equipment control rule, thereby improving the accuracy of the equipment control rule.
The following describes implementation details of the method for generating the control rule of the smart device according to this embodiment in detail, and the following description is only provided for easy understanding and is not necessary for implementing this embodiment.
Wherein the step 12 comprises:
step 121, when the data set is a reported parameter record data set, the feature points include: the method comprises the steps that a first time point reported by first equipment, a first parameter reported by the first equipment, a first numerical value of the first parameter reported by the first equipment, a function type of the first equipment and an equipment identifier of the first equipment are reported; that is, the reported parameter record data set includes a plurality of reported parameter record data, each reported parameter record data includes the above feature point information, and each feature point information can also generate a feature point vector.
Step 122, when the data set is a user operation record data set, the feature points include: a second time point for controlling the second device, a second parameter controlled by the second device, a second numerical value after the second parameter is controlled, a function category to which the second device belongs, and a device identifier of the second device. That is, the user operation record data set includes a plurality of user operation record data, each of which includes the above feature point information, and each of the feature point information may also generate one feature point vector.
In one embodiment, the step 13:
step 131A, calculating similarity between data points in the reported parameter record data set;
step 132A, clustering data points with similarity exceeding a first preset threshold into a class, and generating a reporting parameter record cluster set;
meanwhile, the step 13 further includes:
step 131B, calculating similarity between data points in the user operation record data set;
and step 132B, clustering data points with the similarity exceeding a second preset threshold into a class, and generating a user operation record cluster set.
Wherein, steps 131A and 132A are executed simultaneously with steps 131B and 132B, and step 131A is similarly described below, and steps 131B and 131A are similar and will not be described repeatedly.
The step 131A includes the steps of:
sim1＝θsimt+β1simp1+β2simp2+λ1simd1+λ2simd2；
wherein sim1 is the similarity between data points in the reported parameter record dataset;
simt is the first time similarity between data points in the reported parameter record dataset;
simp1the first parameter similarity between data points in the reported parameter record data set;
simp2the parameter content similarity of the first parameter between data points in the reported parameter record data set;
simd1is the functional category similarity between data points in the reported parameter record dataset,
simd2is the device identity similarity between data points in the reported parameter record dataset,
α is the weight of the first time similarity, β 1 is the weight of the first parameter similarity, β 2 is the weight of the first numerical similarity, lambda 1 is the weight of the function class similarity, and lambda 2 is the weight of the equipment identification similarity.
The calculation formula of the time similarity may be:
wherein, t1Is a time point vector of a first data point in the reported parameter record dataset; t is t2Is a time point vector of a second data point in the reported parameter record dataset;
the calculation of the first parameter similarity may be strong correlation, when the first parameter vectors of the first data point and the second data point are consistent, the first parameter similarity is 1, otherwise, the first parameter similarity is 0;
when the parameter content of the first parameter is a parameter value, the calculation formula of the similarity of the parameter content of the first parameter may be:
said p is1A first parameter value vector that is a first data point; p is a radical of2Is a first vector of parameter values for a second data point;
when the parameter content of the first parameter is in a parameter state, calculating the similarity of the parameter content of the first parameter into strong correlation, when the states of the first data point and the second data point are consistent, the similarity of the parameter content of the first parameter is 1, otherwise, the similarity is 0;
the function category similarity calculation may be a strong correlation, and when the function category vectors of the first data point and the second data point are consistent, the function category similarity is 1, otherwise, the function category similarity is 0;
the calculation of the device identification similarity may be a strong correlation, and when the device identifications of the first data point and the second data point are consistent, the device identification similarity is 1, otherwise, the device identification similarity is 0.
Wherein step 14 comprises:
step 141, calculating the similarity between the clustering points in the reported parameter record clustering set and the clustering points in the user operation record clustering set;
and 142, clustering the data points with the similarity exceeding a third preset threshold into a class to obtain a device behavior related cluster set.
Step 141 includes:
sim2＝αsimt+βsimk+λ1(simd1+simp1)+λ2(simd2+simp2)
wherein sim2 is the similarity between a first clustering point in the reported parameter record clustering set and a second clustering point in the user operation record clustering set;
simtis time between cluster pointsInter-similarity;
simkis the similarity between clusters;
simd1is the functional category similarity between the clustering points;
simd2is the device identification similarity between the cluster points;
simp1is the parameter similarity between the clustering points;
simp2is the parameter content similarity between the clustering points;
α is the weight of time similarity, β is the weight of cluster similarity, λ 1 is the weight of function category similarity and parameter similarity, and λ 2 is the weight of device identification similarity and parameter content similarity.
The calculation formula of the time similarity between the clustering points is as follows:
wherein, t1Is a time point vector of the first cluster point; t is t2Is a time point vector of the second cluster point;
calculating the similarity between clusters to be strong correlation, and when the first cluster point and the second cluster point are both from the cluster set of data reported by equipment, the similarity between clusters is 0, otherwise, the similarity is 1;
calculating the function category similarity between the clustering points into strong correlation, wherein when the function category vectors of the first clustering point and the second clustering point are consistent, the function category similarity is 0, otherwise, the function category similarity is 1;
calculating the similarity of the equipment identifications among the clustering points into strong correlation, wherein when the equipment identification vectors of the first clustering point and the second clustering point are consistent, the similarity of the equipment identifications is 0, otherwise, the similarity of the equipment identifications is 1;
calculating the parameter similarity between the clustering points to be strong correlation, wherein when the parameter vectors of the first clustering point and the second clustering point are consistent, the parameter similarity between the clustering points is 0, otherwise, the parameter similarity is 1;
when the parameter content of the first parameter is a numerical value, the parameter is in-parameterThe calculation formula of the similarity of the volume similarity is as follows:
said p is1Is a first parameter value vector for the first cluster point; p is a radical of2A first vector of parameter values for the second cluster point;
and when the parameter content of the first parameter is in a state, calculating the similarity of the parameter content into strong correlation, when the parameter states of the first clustering point and the second clustering point are consistent, calculating the similarity of the parameter content into 0, otherwise, calculating the similarity into 1.
The feature vector of the first clustering point in the reported parameter record clustering set specifically includes:
the time point vector is: the average of all times for the data points in each cluster;
the parameter vector is: all data points in each cluster contain the largest number of first parameters;
the first parameter value vector is: an average of the first parameter values of the largest number of first parameters contained by all data points in each cluster;
the function class vector is: all data points in each cluster contain the largest number of functional categories;
the device identification vector is: all data points in each cluster contain the largest number of device identifications.
In the embodiment, similarity calculation and cluster analysis are respectively performed on parameter reporting data of equipment under an account and instruction data issued by a user to the equipment by the user in the personalized scene of the respective smart home to collect behavior habits of the user for operating the smart equipment, so that the platform directionally pushes smart equipment linkage rules which accord with the user operation scene to the user by taking the behavior habits of the user as an analysis basis, the intelligent degree of home equipment control can be continuously improved, the user operation is simplified, and the use of the smart home of the user is improved.
Optionally, in an embodiment, the step 15 includes:
step 151, selecting a preset number of clustering points in the device behavior related clustering set according to the number and size sequence of data contained in the clustering points; the steps can also be as follows: filtering out clustering points only containing reported parameter record data from the device behavior related clustering set to generate residual clustering points; and selecting a preset number of clustering points from the residual clustering points according to the sequence of the number and the size of data contained in the clustering points. Therefore, interference of unnecessary data can be removed.
Step 152, when the selected clustering point only contains the user operation record data, generating a rule according to the user operation record data contained in the clustering point: controlling the second parameter of the second device to the second value at the second point in time; the time points and the numerical values of the parameters in the rule are determined according to the characteristic point vectors carried by the user operation record data contained in the clustering points.
Step 153, when the selected cluster point includes both reporting parameter record data and user operation record data, generating a linkage rule according to the reporting parameter record data and the user operation record data included in the cluster point: and when the data value of the first parameter reported by the first equipment at the first time point is a first numerical value, issuing a linkage rule for controlling the second parameter according to the parameter content to the second equipment at the second time point. The time points and the numerical values of the parameters in the rule are determined according to the user operation record data contained in the clustering points and the characteristic point vectors carried in the user operation record data. For example, controlling the second parameter according to the parameter content may be: and adjusting the parameter state of the second parameter, or adjusting the parameter value of the second parameter.
The method for recommending the intelligent equipment linkage rules based on the user behavior habits is provided, the user behavior habits are learned and the parameter data reported by the analysis equipment are combined, and the intelligent equipment linkage rules with individuation can be directionally recommended. The method has the advantages that the equipment operation records of the user are recorded, the reason for the user to operate the equipment is researched and analyzed by combining the parameter reported data records of the equipment, the behavior habits of the user to operate the equipment are extracted, namely, the parameter data sets reported by the equipment in the intelligent home and the records of the user equipment operation are collected, so that the behavior habits of the user are analyzed, and the incidence relation between the behavior habits and the equipment monitoring parameters is researched, so that the personalized equipment linkage rules are recommended for the user in a directional mode, the intelligence of the whole intelligent home equipment control can be improved, and the complexity of the user in controlling the intelligent home equipment is simplified.
The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.
The following describes an application scenario of the present invention. The application scenario provides an algorithm for recommending the intelligent equipment linkage rule based on the user behavior habit, which is applied to linkage control of intelligent homes and intelligent equipment scenes, and can quickly realize self-learning of the intelligent equipment scene linkage rule and directionally recommend the intelligent equipment scene linkage rule by collecting and analyzing the use behavior habit of the user on the intelligent equipment in the intelligent home scene.
The specific flow of the application scenario is shown in fig. 2, and the specific flow steps are as follows:
the first step is as follows: collecting a parameter data set reported by equipment and a user equipment operation data set;
the second step is that: and respectively extracting characteristic points such as reporting or operating time points, device affiliated classifications, device reporting or operating parameters, device identifications and the like from the collected device reporting parameter data sets and user device operating data sets.
The third step: and respectively carrying out cluster analysis on the device parameter reporting data set and the user equipment operation data set according to the obtained characteristic vector.
After receiving a parameter data set reported by equipment or an operation data set of user equipment, respectively calculating the similarity between data points in the two data sets according to a formula 1, and clustering the data points with the similarity exceeding 0.9 into one class. After the clustering result is generated, the average time of the data points in each cluster, the most equipment identifiers and the most reported or most operated parameters of the equipment identifiers are used as the characteristic vectors of the clustering points.
The following describes a similarity calculation method for reporting parameter data sets and user equipment operation data sets by the device during the clustering algorithm.
sim1＝θsimt+β1simp1+β2simp2+λ1simd1+λ2simd2(1)
Wherein, simtIs the time similarity of the data points, calculated according to equation (2);
wherein, t1Is the point in time of the first data in the data set; t is t2Is the point in time of the second data in the data set. The closer the time points are to the two data, the higher the temporal similarity.
simp1The similarity of the reported parameters of the data point equipment is calculated as strong correlation: the consistent parameter similarity is 1, otherwise, the parameter similarity is 0;
simp2similarity of parameter values of reported parameters of data point equipment;
simd1is the similarity of the classifications of the devices in the data points, where the similarity is calculated as a strong correlation, the consistent device classification similarity is 1, otherwise 0.
simd2Is the similarity of the identities of the devices in the data points, where the similarity is calculated as a strong correlation, and the consistent device identity similarity is 1, otherwise 0.
α, β 1, β 2, λ 1, λ 2 are the time similarity, parameter content similarity, device classification similarity, and weight of device identification similarity in calculating the total similarity, respectively, and the values can be set according to actual conditions.
The fourth step: and taking each cluster point in the equipment reporting parameter data cluster set and the user equipment operation data cluster set obtained by analysis in the third step as a data point, calculating the correlation between the cluster points in the two cluster sets, and carrying out secondary clustering to obtain the final equipment behavior related cluster.
The association between the device reported parameter data cluster set and the user equipment operation record cluster set can be performed by calculating the similarity through a clustering algorithm. And aiming at the cluster points in the data cluster set reported by the equipment and the operation cluster set of the user equipment, calculating the similarity between the data points according to a formula 3, and clustering the data points with the similarity exceeding 0.9 into one class.
sim2＝αsimt+βsimk+λ1(simd1+simp1)+λ2(simd2+simp2) (3)
Wherein, simtIs the time similarity of the data points, calculated according to equation (2);
simkthe similarity from the clusters is calculated as strong correlation, the similarity from the data cluster set reported by the equipment or the user operation excitation cluster set is 0, otherwise, the similarity is 1;
simp1the parameter similarity is strong relevance, the parameter similarity which is different from 5.3.2 is consistent with 0, otherwise, the parameter similarity is 1, and the interference of reporting the equipment parameters and receiving the equipment parameters, which is caused by equipment control, is eliminated.
simp2Is the parameter content similarity between the clustering points;
simd1the classification of the equipment in the data points, the similarity is calculated as strong correlation, different from the above, the similarity of the consistent equipment classification identification is 0, otherwise, the similarity is 1, in order to eliminate the interference of the same equipment;
simd2is the device identification similarity between the cluster points;
α, β 1, β 2, λ 1, λ 2 are the weights of time similarity, parameter content similarity, classification similarity, and device identifier similarity in calculating the total similarity, and can be specifically set according to the actual situation.
The fifth step: converting the result of the fourth step into a linkage rule, that is: and the related results of the cluster reported by the equipment and the cluster operated by the user are converted into a linkage rule algorithm. The method specifically comprises the following steps:
analyzing each clustering point in the result set, discarding the clustering points only reported by the device parameters, and selecting a predetermined number (for example, three) of clustering points with the largest number in the clustering sets from the remaining clustering points, if:
(1) only the user operation device record is included, so that the user may be accustomed to control a certain device at a certain time point, and a rule for making a fixed operation instruction sent to the device at fixed time can be recommended to the user.
(2) The device parameter reporting data and the user operation device record are contained, so that the reporting data of the device possibly drives a user to issue an operation command to another device, and a linkage rule for issuing the operation command to the other device when the device reports the parameter data can be recommended to the user.
And a sixth step: and filtering out the rules established by the user from the equipment linkage rule set which is generated in the fifth step and accords with the user behavior habits so as to eliminate the interference of the rules established by the user on the analysis result, and recommending the rest rules to the user.
At present, according to basic functions of equipment and popular linkage scenes of the equipment, equipment linkage rules are preset on a platform in a mode for users to select and use, personalization is lacked, personalized customization of the equipment linkage rules can not be carried out on the users according to the types, numbers, related ranges and installation places of intelligent equipment owned by the users, and the equipment linkage rules preset on the platform can be greatly set due to the fact that one or more factors such as the types, the numbers and the installation places of the user equipment are inconsistent or can not reach expected effects after setting.
In addition, the popular smart home design in the market is limited to remote control and remote monitoring of a single device, few smart homes realize linkage strategy configuration and execution among the smart devices, and the linkage strategy configuration and execution depend on that a user has certain understanding and activity on linkage of the smart home and the smart devices, and the linkage strategy configuration and execution lack certain individuation relative to the user. And the current situation also has certain hindering influence on the popularization and the popularization of the smart home.
In addition, in the prior art, most of the methods for realizing the linkage between the intelligent devices in an intelligent home allow users to actively set up and create device linkage rules according to scenes and habits of using the intelligent devices at ordinary times, or preset linkage strategies on a platform for the users to select and set. The two methods require that the user has certain comprehensiveness and activity on the intelligent home scene application and the intelligent device linkage, and the real intellectualization of the home device linkage is not achieved.
The embodiment improves the above technologies, and specifically includes:
1. the embodiment provides the intelligent equipment linkage rule recommended based on the user behavior habit, the user equipment operation record is combined with the parameter data reported by the equipment to analyze the relation between the equipment in the intelligent home, and the correlation between the driving force and the execution action of the user operation equipment is found, so that an equipment linkage rule set suitable for the user is obtained and is recommended to the user in a directional mode, the behavior habit of the user is represented, the equipment use effect in an actual scene is met, the complexity of the user in operating the intelligent equipment can be simplified, the intelligent degree of the home can be improved, and the intelligent equipment linkage rule is suitable for the current intelligent home business.
2. The embodiment provides the calculation method suitable for the similarity of the data points in the equipment parameter reporting data set and the user equipment operation data set, and the calculation method is used as the basis of data clustering and analysis, thereby laying a foundation for analyzing user behavior habits and calculating appropriate equipment linkage rules. Namely, a calculation method for analyzing the incidence relation between the reported data of the equipment and the operation of the user equipment is provided, so that the driving force behind the behavior of the user operation equipment is explored, the driving force and the execution action of the equipment operation are converted into an equipment linkage rule and recommended to the user, the user operation is simplified, and the intelligent degree of home furnishing is improved.
Another embodiment of the present invention relates to a device for generating a control rule of an intelligent device, as shown in fig. 3, including:
the collection unit is used for collecting at least one reporting parameter record data set of the first equipment and at least one user operation record data set of the second equipment; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices;
the extraction unit is used for respectively extracting the characteristic points of the reported parameter record data set and the user operation record data set;
the first clustering unit is used for clustering the reported parameter record data set according to the extracted feature points to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
the second clustering processing unit is used for calculating the correlation between the clustering points in the reported parameter record clustering set and the clustering points in the user operation record clustering set, and clustering the parameter record clustering set and the user operation record clustering set according to the correlation to obtain a device behavior related clustering set;
a rule conversion unit which converts the device behavior related cluster set into a device control rule;
an output unit that outputs the device control rule.
It should be understood that this embodiment is an example of the apparatus corresponding to the first embodiment, and may be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment.
It should be noted that each module referred to in this embodiment is a logical module, and in practical applications, one logical unit may be one physical unit, may be a part of one physical unit, and may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, elements that are not so closely related to solving the technical problems proposed by the present invention are not introduced in the present embodiment, but this does not indicate that other elements are not present in the present embodiment.
A fifth embodiment of the present invention is directed to a terminal, as shown in fig. 4,
the method comprises the following steps:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of generating control rules for a smart device.
Where the memory and processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting together one or more of the various circuits of the processor and the memory. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory may be used to store data used by the processor in performing operations.
Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (13)
1. A method for generating a control rule of an intelligent device is characterized by comprising the following steps:
collecting at least one reported parameter record data set of first equipment and at least one user operation record data set of second equipment; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices;
respectively extracting characteristic points of the reported parameter record data set and the user operation record data set;
according to the extracted feature points, clustering the reported parameter record data set to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
calculating the correlation between the cluster points in the reported parameter record cluster set and the cluster points in the user operation record cluster set, and carrying out cluster processing on the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set;
converting the device behavior related cluster set into a device control rule;
and outputting the equipment control rule.
2. The method according to claim 1, wherein the step of performing feature point extraction on the reported parameter record data set and the user operation record data set respectively comprises:
when the data set is a reported parameter record data set, the feature points include: the method comprises the steps that a first time point reported by first equipment, a first parameter reported by the first equipment, parameter content of the first parameter reported by the first equipment, a function category to which the first equipment belongs and an equipment identifier of the first equipment are obtained; the parameter content of the first parameter comprises: a parameter value or a parameter state of a first parameter;
when the data set is recorded for user operation, the feature points include: a second time point for controlling the second device, a second parameter controlled by the second device, parameter content after the second parameter is controlled, a function category to which the second device belongs, and a device identifier of the second device, where the content of the second parameter includes: a parameter value or a parameter status of the first parameter.
3. The method of claim 2, wherein the step of converting the set of device behavior related clusters into device control rules comprises:
in the device behavior related clustering set, selecting a preset number of clustering points according to the number and size sequence of data contained in the clustering points;
when the selected clustering point only contains user operation record data, generating a rule according to the user operation record data contained in the clustering point: controlling the second parameter of the second device to the second value at the second point in time;
when the selected clustering point contains reporting parameter record data and user operation record data, generating a linkage rule according to the reporting parameter record data and the user operation record data contained in the clustering point: and when the data value of the first parameter reported by the first equipment at the first time point is a first numerical value, issuing a linkage rule for controlling the second parameter according to the parameter content to the second equipment at the second time point.
4. The method of claim 3, wherein the step of selecting a predetermined number of cluster points in the device behavior related cluster set in order of magnitude of the amount of data contained in the cluster points comprises:
filtering out clustering points only containing reported parameter record data from the device behavior related clustering set to generate residual clustering points;
and selecting a preset number of clustering points from the residual clustering points according to the sequence of the number and the size of data contained in the clustering points.
5. The method of claim 1,
the step of clustering the reporting parameter record data set according to the extracted feature points to obtain a reporting parameter record cluster set comprises: calculating the similarity between data points in the reported parameter record data set; clustering data points with similarity exceeding a first preset threshold into a class, and generating a reporting parameter record cluster set;
the step of clustering the user operation record data set according to the extracted feature points to obtain a user operation record cluster set comprises: calculating the similarity between data points in the user operation record data set; and clustering data points with the similarity exceeding a second preset threshold into a class to generate a user operation record cluster set.
6. The method of claim 5,
the step of calculating the similarity between the data points in the reported parameter record dataset comprises:
wherein sim1 is the similarity between data points in the reported parameter record dataset;
simt is the first time similarity between data points in the reported parameter record dataset;
simp1the first parameter similarity between data points in the reported parameter record data set;
simp2similarity of parameter content of a first parameter between data points in the reported parameter record data set;
simd1is the functional category similarity between data points in the reported parameter record dataset,
simd2is the device identity similarity between data points in the reported parameter record dataset,
α is the weight of the first time similarity, β 1 is the weight of the first parameter similarity, β 2 is the weight of the first numerical similarity, lambda 1 is the weight of the function class similarity, and lambda 2 is the weight of the equipment identification similarity.
7. The method of claim 6,
the calculation formula of the time similarity is as follows:
wherein, t1Is a time point vector of a first data point in the reported parameter record dataset; t is t2Is a time point vector of a second data point in the reported parameter record dataset;
the calculation of the first parameter similarity is strong correlation, when the first parameter vectors of the first data point and the second data point are consistent, the first parameter similarity is 1, otherwise, the first parameter similarity is 0;
when the parameter content of the first parameter is a parameter value, the calculation formula of the similarity of the parameter content of the first parameter is as follows:
said p is1A first parameter value vector that is a first data point; p is a radical of2Is a first vector of parameter values for a second data point;
when the parameter content of the first parameter is in a parameter state, calculating the similarity of the parameter content of the first parameter into strong correlation, when the states of the first data point and the second data point are consistent, the similarity of the parameter content of the first parameter is 1, otherwise, the similarity is 0;
the function category similarity is calculated as strong correlation, when the function category vectors of the first data point and the second data point are consistent, the function category similarity is 1, otherwise, the function category similarity is 0;
the calculation of the device identification similarity is strong correlation, when the device identifications of the first data point and the second data point are consistent, the device identification similarity is 1, otherwise, the device identification similarity is 0.
8. The method of claim 1, wherein the step of calculating a correlation between cluster points in the reported parameter record cluster set and cluster points in the user operation record cluster set, and performing cluster processing on the parameter record cluster set and the user operation record cluster set according to the correlation to obtain a device behavior related cluster set comprises:
calculating the similarity between the clustering points in the reported parameter record clustering set and the clustering points in the user operation record clustering set;
and clustering the data points with the similarity exceeding a third preset threshold into a class to obtain a device behavior related cluster set.
9. The method of claim 8, wherein the step of calculating the similarity between the cluster points in the reporting parameter record cluster set and the cluster points in the user operation record cluster set comprises:
sim2＝αsimt+βsimk+λ1(simd1+simp1)+λ2(simd2+simp2)
wherein sim2 is the similarity between a first clustering point in the reported parameter record clustering set and a second clustering point in the user operation record clustering set;
simtis the temporal similarity between the cluster points;
simkis the similarity between clusters;
simd1is the functional category similarity between the clustering points;
simd2is the device identification similarity between the cluster points;
simp1is the parameter similarity between the clustering points;
simp2is the parameter content similarity between the clustering points;
α is the weight of time similarity, β is the weight of cluster similarity, λ 1 is the weight of function category similarity and parameter similarity, and λ 2 is the weight of device identification similarity and parameter content similarity.
10. The method of claim 9,
the calculation formula of the time similarity between the clustering points is as follows:
wherein, t1Is a time point vector of the first cluster point; t is t2Is a time point vector of the second cluster point;
calculating the similarity between clusters to be strong correlation, and when the first cluster point and the second cluster point are both from the cluster set of data reported by equipment, the similarity between clusters is 0, otherwise, the similarity is 1;
calculating the function category similarity between the clustering points into strong correlation, wherein when the function category vectors of the first clustering point and the second clustering point are consistent, the function category similarity is 0, otherwise, the function category similarity is 1;
calculating the similarity of the equipment identifications among the clustering points into strong correlation, wherein when the equipment identification vectors of the first clustering point and the second clustering point are consistent, the similarity of the equipment identifications is 0, otherwise, the similarity of the equipment identifications is 1;
calculating the parameter similarity between the clustering points to be strong correlation, wherein when the parameter vectors of the first clustering point and the second clustering point are consistent, the parameter similarity between the clustering points is 0, otherwise, the parameter similarity is 1;
when the parameter content of the first parameter is a numerical value, the calculation formula of the similarity of the parameter content is as follows:
said p is1Is a first parameter value vector for the first cluster point; p is a radical of2A first vector of parameter values for the second cluster point;
when the parameter content of the first parameter is in a state, calculating the similarity of the parameter content into strong correlation, when the parameter states of the first clustering point and the second clustering point are consistent, calculating the similarity of the parameter content into 0, otherwise, calculating the similarity into 1;
the feature vector of the first clustering point in the reported parameter record clustering set specifically includes:
the time point vector is: the average of all times of cluster points in each cluster;
the parameter vector is: all cluster points in each cluster contain the most number of first parameters;
the parameter content vector is: the average value of the first parameter values of the first parameters with the largest number contained in all the clustering points in each cluster; or the parameter state with the largest number contained in all the clustering points in each cluster;
the function class vector is: all data points in each cluster contain the largest number of functional categories;
the device identification vector is: all data points in each cluster contain the largest number of device identifications.
11. An apparatus for generating a control rule of an intelligent device, comprising:
the collection unit is used for collecting at least one reporting parameter record data set of the first equipment and at least one user operation record data set of the second equipment; the first equipment and the second equipment belong to the same account of the intelligent home system; the first device and the second device are the same device or different devices;
the extraction unit is used for respectively extracting the characteristic points of the reported parameter record data set and the user operation record data set;
the first clustering unit is used for clustering the reported parameter record data set according to the extracted feature points to obtain a reported parameter record cluster set; according to the extracted feature points, clustering the user operation record data set to obtain a user operation record cluster set;
the second clustering processing unit is used for calculating the correlation between the clustering points in the reported parameter record clustering set and the clustering points in the user operation record clustering set, and clustering the parameter record clustering set and the user operation record clustering set according to the correlation to obtain a device behavior related clustering set;
a rule conversion unit which converts the device behavior related cluster set into a device control rule;
an output unit that outputs the device control rule.
12. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of generating control rules for a smart device according to any of claims 1 to 10.
13. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for generating the control rule of the smart device according to any one of claims 1 to 10.
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